CNN-based methods have dominated the ReID community for many years. However, pure-transformer models are gradually becoming a popular choice. He  [1]} are the first to successfully apply ViTs to ReID tasks by proposing TransReID which achieves state-of-the-art performance on both person and vehicle ReID. Auto-Aligned Transformer (AAformer) [2]} also uses ViT backbone with the additional learnable vectors of “part tokens" to learn the part representations and integrates the part alignment into the self-attention. Other works try to use Transformer to aggregate features or information from CNN backbones. For example, [3]}, [4]}, [5]} integrate Transformer layers into the CNN backbone to aggregate hierarchical features and align local features. For video ReID, [6]}, [7]} exploit Transformer to aggregate appearance features, spatial features, and temporal features to learn a discriminative representation for a person tracklet.
